Abstract Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have
revolutionized the field of advanced robotics in recent years. AI, ML, and DL are transforming …

With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Learning-based infrared small object detection methods currently rely heavily on the
classification backbone network. This tends to result in tiny object loss and feature …

Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras
and LiDAR, have significant potential in precision agriculture, including object detection …

Traffic monitoring is of outmost importance in today's modern world. In the past, stationary
data collectors such as video cameras and induction loops were used for this task. However …

With the rapid development of object detection technology for unmanned aerial vehicles
(UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide …

Space-air-ground integrated networks (SAGINs) are key elements for facilitating high-speed
seamless connectivity to the devices/users in infrastructure-less environments, where the …

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning
technologies, the application of UAV-based object detection has become increasingly …

Deep neural networks have achieved great success in many important remote sensing
tasks. Nevertheless, their vulnerability to adversarial examples should not be neglected. In …

The detection of infrared vehicle targets by UAVs poses significant challenges in the
presence of complex ground backgrounds, high target density, and a large proportion of …